Exploring Explanation Effects on Consumers’ Trust in Online Recommender Agents

ABSTRACT

This paper explores how explanations within an online recommender agent (RA) affect consumers’ trust in the RA and their willingness to adopt its recommendations. The paper proposes a theoretical research model which employs a multidimensional notion of trust and considers the effects of varying two features of the RA: explanation availability, i.e., providing, or not, an explanation of the underlying reasoning process of an RA; and explanation mode, i.e., using graphs or text as the means of providing explanations. In addition, we investigate the impact of a consumer’s perceived level of personalization of the RA as a measured variable. Two dimensions of trust—trust in integrity and trust in competence—along with intention to accept the recommendations serve as the dependent variables within the model. A within-subject experiment is conducted with four RAs. Empirical data provide evidence that explanation availability, explanation mode, and perceived personalization all have significant influence on consumers’ trust beliefs in an RA and willingness to adopt its recommendations. Effects of both the availability and mode of explanations on consumers’ trust beliefs are found to be mediated by consumers’ perceived personalization of the RA that, in turn, mediate the effects on intention to use.

Additional information

Notes on contributors

Jingjing Zhang

Jingjing Zhang is an assistant professor of Operations and Decision Technologies at the Kelley School of Business, Indiana University. She received her Ph.D. in Information Systems from the University of Minnesota. Her research interests include machine learning, data mining, recommender systems, mobile applications, and human–computer interactions. She has published in journals such as Information Systems Research, IEEE Transaction on Knowledge and Data Engineering, INFORMS Journal on Computing, ACM Transactions on Information Systems, and ACM Transactions on Management Information Systems.

Shawn P. Curley

Shawn P. Curley is a professor of Information and Decision Sciences at the Carlson School of Management, University of Minnesota. He received his Ph.D. in Psychology from the University of Michigan. His research interests are in behavioral decision theory and include the effects of feedback on behavior in combinatorial auctions, and user behavior with recommender systems. Recent research outlets include Management Science, Information Systems Research, MIS Quarterly, Organizational Behavior and Human Decision Processes, and Journal of Behavioral Decision Making.